A complete technical breakdown of Fast R-CNN and Faster R-CNN, covering RoI Pooling, quantization effects, Region Proposal Networks, anchor boxes, IoU labeling, multi-task loss, and why replacing Selective Search with RPN transformed object detection into a fully end-to-end trainable two-stage architecture.

Unlock the mechanics of Object Detection with our deep dive into R-CNN. Moving beyond simple image classification, this guide explores how machines localize objects using Bounding Boxes, Selective Search, and Support Vector Machines. Whether you are calculating IoU or understanding the transition from sliding windows to smart proposals, this article covers the complete R-CNN architecture and evaluation metrics.